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Knowledge Topic Confusion Matrix
x402
Knowledge Topic Confusion Matrix is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Computes a confusion matrix for knowledge-topic text classification (setup/usage/troubleshooting/reference) from up to 200 labelled records with actual and predicted labels.
Price / call$0.01
Success—
StatusUnknown
How this service works
Knowledge topic - Confusion matrix: Confusion matrix for Knowledge topic. Up to 200 supplied labelled text records. Local dataset tools, evaluation and small naive-Bayes baselines; no hosted model or foundation-model training. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded confusion matrix deliverable.
Output
A bounded confusion matrix deliverable showing how predicted knowledge-topic labels (setup, usage, troubleshooting, reference) align with actual labels across the supplied records, enabling identification of misclassification patterns between topic categories.
Example use cases
Evaluate documentation classifier accuracy
“I've got 120 labelled docs — each has an actual topic (setup, usage, troubleshooting, or reference) and what my classifier predicted. Can you build a confusion matrix so I can see which categories it's mixing up?”
Audit NLP model before production release
“Before I ship my knowledge-base topic model, I need a confusion matrix from these 200 test records with their actual and predicted labels — setup, usage, troubleshooting, or reference — so I can spot any systematic errors.”
Compare two classifier outputs on same dataset
“I ran two different classifiers on 80 articles and saved the predictions. Can you generate a confusion matrix for the first classifier's predictions against the actual labels so I can benchmark it?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-knowledge-topic-confusion?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, low-cost ($0.01) confusion matrix evaluation for a 4-class knowledge-topic text classifier (setup/usage/troubleshooting/reference) without needing to host a model or use a foundation model. Ideal for offline batch evaluation of up to 200 pre-labelled records where you already have both actual and predicted labels.
Side effects
- Exceeds 200 record limit — request rejected
- Missing required fields (id, text, actual, predicted) — validation error
- Invalid enum value for actual or predicted label — schema error
- Empty records array — no matrix computable
- Payment failure — endpoint returns 402 without processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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